How to plot vertical scatter using only matplotlib

Question

I need to plot vertical scatters in matplotlib but I couldn't find anything in matplotlib.org/examples or StackOverflow.

I tried something of my own but I am missing Jitter. Jitter changes X component slightly for the points having same (or very similar) Y components so they won't overlap. Is there anything which I can use or will I have to change x components manually?

import numpy as np
from matplotlib import pyplot as plt

x = np.array([1,2,3])
l = ['A','B','C']
a = np.array([2,2,3])
b = np.array([3,3,4])
c = np.array([7,7,5])
d = (np.array(a) + np.array(b) + np.array(c)) / 3

plt.subplot(111)
plt.margins(0.2)
plt.xticks(x,l)
plt.plot(x, a, 'ro', label='a')
plt.plot(x, b, 'ro', label='b')
plt.plot(x, c, 'ro', label='c')
plt.plot(x, d, 'k_', markersize=15, label='avg')
plt.tight_layout()
plt.savefig('vertical_scatter')
plt.close()

which gave me following

I found this on Seaborn.

which is what I want but only using matplotlib.

I'd like to suggest you mention you want to "keep it to matplotlib only" in your question. — Y. Luo
– Y. Luo, Commented Jul 8, 2017 at 5:55
Isn't your Matplotlib example doing the same thing as the intended plot? — Nipun Batra
– Nipun Batra, Commented Jul 8, 2017 at 8:35
People often use seaborn for cases where they don't need to but in this case it seems absolutely appropriate and @YLuo's now deleted answer is actually working nicely. As it stands this answers the question and I would actually be tempted to undelete that answer. If you don't want to use use seaborn, please be very specific in your question about what you want to use and why. State clearly what you mean by "jitter". — ImportanceOfBeingErnest
– ImportanceOfBeingErnest, Commented Jul 8, 2017 at 9:43
@ImportanceOfBeingErnest Sir, the Seaborn example solves my problem entirely and I don't have anything against using Seaborn. I was just wondering whether it was possible with only using Matplotlib. — cheesebread
– cheesebread, Commented Jul 8, 2017 at 10:02

ImportanceOfBeingErnest · Accepted Answer · 2017-07-08 11:03:43Z

7

An example with jitter using only matplotlib would be the following. The idea is basically to add some random noise to the x values.

import numpy as np
import matplotlib.pyplot as plt

data = np.random.rayleigh(scale=1, size=(30,4))
labels = list("ABCD")
colors = ["crimson", "purple", "limegreen", "gold"]

width=0.4
fig, ax = plt.subplots()
for i, l in enumerate(labels):
    x = np.ones(data.shape[0])*i + (np.random.rand(data.shape[0])*width-width/2.)
    ax.scatter(x, data[:,i], color=colors[i], s=25)
    mean = data[:,i].mean()
    ax.plot([i-width/2., i+width/2.],[mean,mean], color="k")

ax.set_xticks(range(len(labels)))
ax.set_xticklabels(labels)

plt.show()

answered Jul 8, 2017 at 11:03

ImportanceOfBeingErnest

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Comments

Michael H. · Accepted Answer · 2017-07-08 11:08:55Z

1

Like I mentioned in my comment, you could shift the x-values according to the distance of neighboring y-points. Smaller distances should be mapped to a larger x-shift. This can be done with a logarithm or another function doing that.

import numpy as np
import matplotlib.pyplot as plt

n = 100
y = np.random.random(n)
x = np.ones(n)
x0 = x[0]

y = np.sort(y)
dist = np.diff(y)  # has one entry less than y
dist = np.hstack([dist, np.median(dist)])  # add random value to match shapes
x = np.log(dist)
x = (x - np.min(x)) / (np.max(x) - np.min(x))  # mapped to range from 0 to 1
x = x0 + 0.5*(x - 0.5)  # mapped to range from x0-1/4 to x0+1/4

plt.scatter(x,y)
plt.scatter(x+1,y)
plt.scatter(x+2,y)

plt.show()

answered Jul 8, 2017 at 11:08

Michael H.

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